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The net effect of using PE data instead of frag data was a significant increase in on-target coverage, which resulted in >90% of the targeted bases covered at 10-fold or higher using jus

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M E T H O D

© 2010 Bainbridge et al.; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Com-mons Attribution License (http://creativecomCom-mons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduc-tion in any medium, provided the original work is properly cited.

Method

Whole exome capture in solution with 3 Gbp of data

Matthew N Bainbridge1,2, Min Wang1, Daniel L Burgess3, Christie Kovar1, Matthew J Rodesch3, Mark D'Ascenzo3,

Abstract

We have developed a solution-based method for targeted DNA capture-sequencing that is directed to the complete human exome Using this approach allows the discovery of greater than 95% of all expected heterozygous singe base variants, requires as little as 3 Gbp of raw sequence data and constitutes an effective tool for identifying rare coding alleles in large scale genomic studies

Background

DNA sequence capture is an effective technique for

enrichment of significant subfractions of the genome for

targeted analysis Sequence capture is generally

con-ducted with either a solid-phase substrate, usually a glass

microarray, or alternatively, in solution [1-4]

Solution-capture performance, however, has not been thoroughly

compared to microarray with respect to uniformity of

representation of the targeted DNA bases or evenness of

DNA sequencing coverage depth between regions [5,6]

Further, solution-based sequence capture has not been

demonstrated to work effectively at the scale of a human

exome (approximately 30 Mbp) but typically has been

limited to targets <5 Mbp in size Despite this, solution

capture has several advantages when compared to

microarrays: the reagent cost is lower; less DNA is

required; and, because the capture method can be

con-ducted entirely in small laboratory tubes, it is readily

scaled and automated Before solution-capture

sequenc-ing can be widely adopted, however, the reproducibility of

the method must first be demonstrated, and targets

should show similar levels of coverage from capture to

capture Ideally, solution-capture methods should also be

able to be coupled to different sequencing technology

platforms, and reliably produce suitable levels of

enrich-ment that routinely enable the discovery of rare genetic variants

This report is the first demonstration of whole exome capture in solution (Table 1) We demonstrate similar lev-els of specificity to microarray-based techniques without sacrificing reproducibility or specificity of either the cap-ture or variant discovery while maintaining all the advan-tages of solution-based techniques over microarray capture

To test the reproducibility of our recent innovations of liquid DNA capture, technical replicate capture experi-ments were performed and subsequently sequenced on the SOLiD [7] platform Capture followed by Illumina [8] sequencing was also performed with both a fragment (frag) and paired-end (PE) library to test the merits of employing PE data versus single-ended reads Finally, we used each of these data sets to test the ability to discover single nucleotide variants across the exome

Results and discussion

Here we report the performance of newly developed methods for sequence-capture in solution The proce-dures were tested with respect to reproducibility of cap-ture, agnosticism to sequencer platform, and the ability to discover genetic variation in human gene coding regions

In total, six captures of the Consensus Coding Sequence (CCDS) exons [9,10] were performed using DNA from one HapMap sample (NA12812) Four separate, replicate capture libraries were prepared for solid sequencing and one additional library was prepared for both the Illumina

* Correspondence: agibbs@bcm.edu

1 Human Genome Sequencing Center, Baylor College of Medicine, One Baylor

Plaza, Houston, Texas 77030, USA

Full list of author information is available at the end of the article

frag and PE sequencing In total, 23 Gbp of data were

uniquely aligned to the human reference genome (Table

S1 in Additional file 1) Variants were called using

algo-rithms tailored to each sequence platform and

corre-sponding data type, and compared to known variants in

the HapMap sample (see Materials and methods)

To first test the reproducibility of the entire

capture-sequencing process, four technical replicate solution

cap-tures were performed Each replicate was sequenced

using one-quarter of the same SOLiD DNA sequencer

'slide' to eliminate run-by-run sequencer variability The

standard procedure for categorizing data from this

sequencing platform is to identify individual sequence

reads that can be mapped at high stringency to a

refer-ence sequrefer-ence These 'mappable' reads constitute the

usually cited yield for each sequence run Here, the

sequences from four technical replicate libraries had an

average of 49.6% (standard deviation 1.23) of mappable

reads derived from the capture target regions with the

remainder mapping elsewhere to the genome This

effi-ciency of properly targeted sequence reads represents a

value similar to Ng et al [3], and higher than Choi et al.

[11] The final DNA sequence coverage across each target

had >98% correlation between all four libraries (Figure 1)

In three of four experiments, >65% of the targeted bases

were covered ten or more times, and the observed

varia-tion in the coverage levels was primarily accounted for by

the total sequence yield of each spot These results

indi-cate that, for a given amount of sequence data, the

aver-age coveraver-age and distribution of coveraver-age is highly

predictable and the performance of each individual target

region in different experiments is consistent

One technical artifact of capture-sequencing

proce-dures is the generation of duplicate DNA sequencing

reads that represent the repeated sequencing of copies of

the same molecule These duplicates generally arise when

there are too few total molecules present at any stage of

the technical manipulations - especially immediately

prior to any PCR step Detection of the duplicate reads by

computational analysis is not trivial, and generally relies

on observation of the alignment positions Unfortunately,

these artifactual duplicates are difficult to distinguish

from exactly overlapping reads that naturally occur within deep sequence samples

The percentage of duplicate reads, defined as reads with the same start point and direction, was approxi-mately 22% for 3 Gbp of aligned data and was also highly consistent between replicates (Table 2, libraries 2 to 4)

As expected, when more reads were generated for the SOLiD library 1 the duplicate rate increased to approxi-mately 33% Simulations of these data showed an expected 'natural' (that is, not PCR-induced) duplication rate of 22 and 14% for 10 Gbp and 3 Gbp of data, respec-tively This means that approximately 33% of our sus-pected duplicate reads are likely not due to PCR-induced duplication, but are those expected from the random dis-tribution of read ends As noted above, it is not possible

to differentiate true duplicate reads from randomly occurring duplicates, and thus all such reads were removed prior to further analysis

To test the merits of using PE reads versus frag reads we generated both types of library for Illumina sequencing Target coverage was slightly more uniform for Illumina than SOLiD; however, both sequence types provide more uniform coverage than other solution capture methods (Figure S1 in Additional file 1) Although the majority of targets had similar normalized coverage when sequenced

by SOLiD or Illumina, approximately equal numbers showed some coverage-bias depending on sequencing platform (Figure 2) Targets with very different coverage levels typically had low complexity and/or high GC con-tent PE sequencing showed both a higher percentage of reads on target and significantly fewer duplicate reads (Table 3) In PE sequencing, reads are generated from both ends of a captured DNA fragment and because the approximate fragment length is known, this information can be used to constrain the alignment of both reads to the human genome Constraining read alignment can greatly improve accuracy when compared to frag sequencing and we hypothesized that these inherent advantages of mapping PE versus single end reads resulted in the increased number of reads derived from the target region We also suspected that the drastic reduction in the duplicate read rate was not because of a difference in library construction but instead the result of

Table 1: A comparison of different capture methodologies

improved informatic identification of 'true' duplicates.

Deep, single-end, frag sequencing quickly saturates the

target regions such that any additional reads will likely

perfectly overlap an existing read and be identified as a

duplicate, even when the reads derive from different

DNA molecules PE sequencing, in contrast, allows us to

use information about both the start and the end of the

capture-DNA fragment in order to determine whether the data are derived from independent DNA molecules Thus, the increased information content of PE data allows us to reduce the misidentification of duplicate reads

To test these theories we also analyzed the PE data as if they were generated from a single-ended frag library

Figure 1 Normalized coverage of replicate SOLiD libraries 2 to 4 versus normalized coverage of replicate library 1 Average coverage for each

target region in library 1 is plotted against library 2 (blue), library 3 (green) and library 4 (red) Coverage for each target is represented as a proportion

of the total sequence generated The line X = Y indicates approximately equal levels of coverage in both libraries.

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

N ormalize Coverage

Library #4 Library #3 Library #2 X=Y

Table 2: Alignment statistics for SOLiD frag sequencing libraries

SOLiD library

a Calculated after duplicate read removal.

This caused the on-target alignment rate to drop slightly

to 73% and the duplicate rate to nearly quadruple to

27.6%, virtually identical to the Illumina frag library

duplicate rate The net effect of using PE data instead of

frag data was a significant increase in on-target coverage,

which resulted in >90% of the targeted bases covered at

10-fold or higher using just 2.8 Gbp of data, a single 2 ×

75 bp lane of Illumina sequencing

To assess the effect of DNA sequencing coverage depth

on our ability to correctly identify variants in the exonic

region of NA12812, we conducted variant discovery

using both approximately 3.3 Gbp and approximately 10

Gbp of SOLiD capture data and 2.8 and 2.5 Gbp of

Illu-mina PE and frag data, respectively Only a subfraction of

these data, non-duplicate sequence reads mapped to

tar-get regions, was used for variant discovery, and it is these

data, not the total, that ultimately affect variant discovery

quality Discovered variants were compared to known

HapMap SNPs in this sample and dbSNP Here, the

con-cordance to HapMap is used to measure the false

nega-tive discovery rate, and the proportion of variants

discovered that were also present in dbSNP129 was used

to approximate the false positive discovery rate Others

have typically found approximately 90% of CCDS variants

to be present in dbSNP for Europeans [3] and significant

deviation below 90% may indicate an increased false

posi-tive discovery rate

Table 4 shows the total amount of data produced for

each sequencing platform, the final amount of data

mapped to targets after duplicate removal and the

con-cordance to dbSNP and HapMap Three giga-base pairs

of SOLiD data allowed discovery of approximately 20 K

variants in the 36-Mbp total capture region for the CCDS

exome Six giga-base pairs of additional sequence data

facilitated the discovery of approximately 20% more

vari-ants In both cases the proportion of variants present in dbSNP129 was high Concordance to all known HapMap variants that occur in the target region was 82% and 92% for 3 and 10 Gbp of raw data, respectively Importantly, this statistic includes known SNPs in regions of little or

no coverage where no variant call is possible When we considered only SNPs that occur in regions with >9× cov-erage, concordance was approximately 95%

Significantly more variants were discovered in the Illu-mina PE data than were found in the frag data (Table 4) and consequently there was also higher concordance at HapMap sites This effect is almost certainly driven by the higher coverage of the target regions achieved by hav-ing PE reads As already noted, this occurs because of a slight increase in reads derived from target, but more sig-nificantly, a drastic reduction in the number of reads that are incorrectly marked as duplicates (Figure 3) Interest-ingly, the proportion of variants that were also in dbSNP was significantly lower in the PE data This may be due to overall lower read quality at the ends of the PE reads and improved mapping of reads that would not have been aligned without a mate Increasing the variant calling stringency (LOD = 8; see Materials and methods) on the

PE data reduced the number of HapMap concordant vari-ants slightly, but improved the percentage of varivari-ants in dbSNP by approximately 3% (Table 4) Although the Illu-mina PE data also had higher HapMap concordance than the SOLiD variant calls, Illumina frag data performed only slightly better than the SOLiD data, despite having significantly more sequence data on target When Hap-Map heterozygous SNP concordance was considered as a function of coverage, SOLiD data out-performed Illu-mina data at low (<9×) coverage (Figure S2 in Additional file 1); however, Illumina consistently obtained 2 to 3% higher concordance at ≥9× coverage The quality of the

Table 3: Alignment statistics for Illumina PE and frag sequencing libraries

a Calculated after duplicate read removal.

Figure 2 Average coverage for each target region as sequenced by Illumina 75-bp frag reads plotted against SOLiD library 1 Coverage for

each target is normalized for the total sequence aligned to target The line X = Y indicates approximately equal levels of coverage in both libraries.

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

SOLiD Coverage (X)

Table 4: Variant discovery and HapMap concordance for different sequencing types and varying amounts of sequence data

variant calls in both data sets was very high, with 22,066

(91.6%) of variants shared between SOLiD 10 G and the

Illumina PE data sets

This work demonstrates the practicality of genomic

tar-get enrichment using capture-sequencing in solution For

the first time, this technology is used at the scale of the

whole exome, comprising over 36 Mbp across >170,000 K

individual targets Using four technical replicate libraries,

we show that the average coverage of the targeted regions

is highly correlated Capture performance is also shown

to be consistent, with the average coverage of each target

having >98% correlation between technical replicates

Thus, it is practical to obtain consistent sequence

cover-age distributions and reproducible variant discovery for a

variety of genomic screening experiments This work also

shows the feasibility of using either SOLiD or

Illumina-based sequencing after capture PE data were shown to be

superior to frag data, increasing both the on-target

num-ber of reads, and greatly improving the correct

identifica-tion of duplicates Development of PE sequencing on the

SOLiD platform should show a similar effect

Interest-ingly, Illumina sequencing consistently shows higher

lev-els of enrichment than SOLiD sequencing This is

unexpected because both sequencing platforms yield

similar coverage distributions in whole genome sequenc-ing data [12], that is, without enrichment Further, the capture-sequencing protocols for both methods are almost identical; therefore, we suspect that differences in efficiency are due to an increase in initial library com-plexity arising from better annealing efficiencies of the Illumina adapter This is probably explained by the fact that Illumina adaptor sequences contain an A/T over-hang, whereas the SOLiD adapters rely on less efficient blunt-end ligation We are currently developing an A/T overhang-type SOLiD sequencing adapter for use in cap-ture to test whether we can improve levels of enrichment for SOLiD-based capture

Finally, we examine the amount of sequence data required to fully interrogate the single nucleotide variants

of the exome in a HapMap sample using either SOLiD or Illumina sequencing Using approximately 10 Gbp of SOLiD data, approximately 93% of all HapMap variants are discovered and over 88% of all variants are present in dbSNP Illumina based sequencing, however, discovers 96% of HapMap variants, with approximately 85% of vari-ants in dbSNP, using only 3 Gbp of sequence data This result is achieved because our Illumina protocol yields higher overall coverage on the target regions even while

Figure 3 Coverage distribution across target regions of SOLiD libraries 1 (10 Gbp) and 2 (3 Gbp) and Illumina PE and frag libraries The

num-ber of bases at each level of coverage for each library type is shown for approximately 10 Gbp of SOLiD data (green), approximately 3 Gbp of SOLiD data (red), approximately 3 Gbp of Illumina PE data (yellow) and approximately 3 Gbp of Illumian frag data (blue) after duplicate removal.

0

500000

1000000

1500000

2000000

2500000

Covergage (X-fold)

SOLiD Library#1 (10G) SOLiD Library#2 (3G) IlluminaPE Illumina Frag

producing less raw sequencing data SOLiD variant

call-ing appears to be more sensitive at <9× coverage, typically

obtaining 20% higher concordance Overall performance

between both platforms is similar, however, and the

majority of the observed difference is likely due to

differ-ences in the variant discovery pipeline software

Conclusions

Sequence capture in solution is easier to automate, has

higher throughput and is less expensive than

microarray-based techniques but has not been extensively used

because of performance issues Here we show the high

reproducibility and scalability of our capture method and

demonstrate that liquid capture can be used in large-scale

experiments to yield reliably high levels of coverage that

are consistent at a target-by-target level and are similar to

microarray-based techniques Further, we establish that

the entire CCDS exome can be interrogated with just 2.8

Gbp of sequence data, approximately 3% of the required

data for whole genome shotgun experiments At this level

of cost and scalability, solution capture-sequencing

becomes an attractive technique for rare variant

discov-ery in its own right and as follow-up to genome-wide

association studies, especially in studies where much of

the heritability of the disease remains unexplained [13]

and thus may be due to rare mutations [14]

Capture-sequencing in solution reduces the cost and increases the

throughput of rare mutation discovery by focusing on

coding regions of the genome and will prove to be a

sig-nificant addition to the geneticist's tool chest

Materials and methods

Sequence data

All sequence data are available from the Short Read

Archive with the following accession numbers

[SRA012614 to SRA012615]

Sequence alignment and variant discovery

SOLiD sequence data were aligned using ABI's

corona_lite package (version: 4.0r2.0) with a maximum

allowed mismatch of 6; all other parameters were set at

default Pileup-style files were generated with samtools

[15] and were filtered to require a variant score of at least

40, or 30 and the variant to be on both strands, and

pres-ent in at least 15% of all reads Illumina data were aligned

using BWA (v 0.5.3) [16] The base quality was

recali-brated using GATK [17] (downloaded 2 October 2009)

Variants were discovered with a minimum LOD of 5

(unless otherwise stated), and were filtered with the

fol-lowing recommended parameters: -X AlleleBalance:low =

0.25, high = 0.75 -X ClusteredSnps

Library and capture

The experimental procedures for preparation of pre- and

post-capture libraries are described in Additional file 1

and are available on-line for the SOLiD [18] and Illumina platforms [19] Briefly, 5 μg genomic DNA is sheared, end-repaired and ligated with either Illumina (frag or PE) platform-specific or SOLiD TM platform-specific adap-tors The library is amplified by pre-capture LM-PCR (linker mediated-PCR) and hybridized to NimbleGen SeqCap EZ Exome libraries After washing, amplification

by post-capture LM-PCR and a quantitative PCR-based quality check, the successfully captured DNA is ready for sequencing

Probe design

The CCDS (build 36.2) exome capture oligonucleotide pool was designed by targeting 174,984 exons of 16,008 high-confidence protein-coding genes in CCDS Chro-mosomal coordinates were obtained from the UCSC genome browser (human build hg18) Target exons were padded to a minimum length of 80 bp, and consolidated

to remove redundant overlaps Coordinates for 528 human miRNA genes were obtained from miRBase (release 10), padded by 25 bp on each end, and likewise consolidated In sum, the coding and miRNA targets comprised 36 Mb of non-redundant sequence, against which 1.9 million probes were selected on the genomic forward strand, with a median probe length of 75 bp and median start-to-start spacing of 34 bp A rebalancing algorithm (described in Additional file 1) was used to improve uniformity of coverage across target exons Probe pools were manufactured by Roche NimbleGen (Madison, WI, USA)

Additional material

Abbreviations

bp: base pair; CCDS: Consensus Coding Sequence; frag: fragment; Gbp: giga-base pairs; Mbp: mega-giga-base pairs; miRNA: microRNA; PE: paired end; SNP: sin-gle nucleotide polymorphism.

Authors' contributions

MNB aided in experimental design, sequence alignment and analysis and drafted the manuscript MW, YQW, IN, DM, and CK conducted the capture hybridization and sequencing DLB and JDJ participated in experimental design and analysis and aided in manuscript preparation MJR, MD'A, JK, and TAR participated in designing the capture reagent TJA and RG participated in experimental design and aided in manuscript preparation.

Acknowledgements

The authors would like to thank Svasti Haricharan for editing the manuscript This project was supported by Award Number U54HG003273 from the National Human Genome Research Institute.

Author Details

1 Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA, 2 Department of Structural and

Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA and 3 Roche NimbleGen, Inc., 504

S Rosa Road Madison, WI 53719, USA

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doi: 10.1186/gb-2010-11-6-r62

Cite this article as: Bainbridge et al., Whole exome capture in solution with 3

Gbp of data Genome Biology 2010, 11:R62

Received: 14 April 2010 Revised: 1 June 2010

Accepted: 17 June 2010 Published: 17 June 2010

This article is available from: http://genomebiology.com/2010/11/6/R62

© 2010 Bainbridge et al.; licensee BioMed Central Ltd

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genome Biology 2010, 11:R62